Indexing for database privacy and anonymization

ABSTRACT

An indexing system uses a cascade of hash structures to process data entries upon ingest for indexing. The indexing system may be used for enhancing database privacy, anonymization, or data compression. A hash structure, for example, a bloom filter or hash table, passes a representation of the data entries to a subsequent hash structure in the cascade responsive to determining that the hash structure previously filtered an instance of the same representation. The indexing system can generate the representations of the data entries using one or more hash functions. A terminal hash structure of the cascade may index the data entries responsive to determining that the data entries satisfy a criteria for anonymization. For instance, the indexing system determines that there exists a threshold number of data entries describing a population of subjects having the same combination of data elements.

TECHNICAL FIELD

The disclosure is related to the field of database indexing and to hashdata structures.

BACKGROUND

Existing database systems may use rules for indexing data entries intodatabases. For example, the rules are used for protecting sensitivedata. If a data entry satisfies a rule, the system may determine toindex the data entry. However, by determining whether the data entrysatisfies the rule, the database system can expose sensitive content ofthe data entry. The technical challenges of indexing and anonymizingdata across sources are barriers to efficient data management operationsand broader discovery of new research opportunities.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed embodiments have advantages and features which will bemore readily apparent from the detailed description, the appendedclaims, and the accompanying figures (or drawings). A brief introductionof the figures is below.

FIG. 1 illustrates an example system environment for a databaseprocessing and management system (DPMS) in accordance with oneembodiment.

FIG. 2 illustrates an example block diagram of an indexing system inaccordance with one embodiment.

FIG. 3A illustrates a diagram of an example cascade of hash structuresin accordance with one embodiment.

FIG. 3B illustrates another diagram of the example cascade of hashstructures shown in FIG. 3A in accordance with one embodiment.

FIG. 3C illustrates yet another diagram of the example cascade of hashstructures shown in FIG. 3A in accordance with one embodiment.

FIG. 3D is a table showing counts of data entries processed by theexample cascade of hash structures shown in FIG. 3A in accordance withone embodiment.

FIG. 3E is a partial log of data entries processed by the examplecascade of hash structures shown in FIG. 3A in accordance with oneembodiment.

FIG. 4 illustrates an example input to a cascade of hash structures inaccordance with one embodiment.

FIG. 5 illustrates an example process flow for anonymizing data entriesin accordance with one embodiment.

FIG. 6 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in one or more processors (or controllers) in accordance with oneembodiment.

DETAILED DESCRIPTION

The Figures (FIGS.) and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof what is claimed.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the disclosed system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

Configuration Overview

An indexing system may provide database privacy by enhancinganonymization of data entries. For example, sensitive data entries to beanonymized may indicate a subject's geographical location or otheridentifying information. The indexing system uses an intentionally lossycompression model that indexes one or more data entries responsive todetermining that the one or more data entries can be anonymized with aset of other data entries. The indexing system may generate arepresentation (e.g., a hash representation) of data entries to input tothe model. In some embodiments, the model includes a cascade of hashstructures to process data entries upon ingest to the indexing system. Ahash structure passes the representation of the data entries to asubsequent hash structure in the cascade responsive to determining thatthe hash structure previously filtered an instance of the samerepresentation. A terminal hash structure of the cascade may index thedata entries responsive to determining that the data entries satisfy acriteria for anonymization. For instance, the indexing system determinesthat there exists a threshold number of data entries describing apopulation of subjects having the same geographical location and diseasediagnosis. Thus, each individual subject of the population cannot beuniquely identified (e.g., by a data element pair) from the othersubjects.

The disclosed embodiments of the indexing system provide a technicalsolution to a challenge unique to computing environments for databaseindexing and anonymization. Existing systems face technical issues inattempting to anonymize sensitive information. For example, anadministrator wants to create a database of subjects located in aparticular geographical location that have been diagnosed with a certaindisease. Additionally, the administrator wants to anonymize the dataentries such that the identity of a specific subject cannot bedetermined from the database. The administrator may count data entriesof subjects upon ingest and choose to index once a predetermined numberof data entries have been checked. However, to check whether a dataentry is associated with the geographical location and disease ofinterest, existing systems may inadvertently expose the sensitive data.The technical solution disclosed herein addresses these and other issuesby providing an indexing algorithm that determines whether to filter orpass representations of data entries through a cascade of hashstructures. The indexing system may use the indexing algorithm forenhancing database privacy, anonymization, data compression, or othersuitable applications related to database processing.

System Overview

FIG. 1 illustrates an example system environment for a databaseprocessing and management system (DPMS) in accordance with oneembodiment. The system environment shown in FIG. 1 includes the DPMS100, client device 120, and one or more databases 140, which areconnected to each other via a network 130. In other embodiments,different or additional entities can be included in the systemenvironment. For example, though only one client device 120 and database140 is shown in FIG. 1, the system environment may include additionalclient devices 120 and/or databases 140. The functions performed by thevarious entities of FIG. 1 may vary in different embodiments.

The DPMS 100 includes a security system 102, connection system 104,indexing system 106, detection system 108, and one or more databases110. Alternative embodiments may include different or additional modulesor omit one or more of the illustrated modules. The indexing system 106indexes data entries for storage in databases and processes queries fordata entries from the databases. The indexing system 106 is furtherdescribed below with reference to FIGS. 2-5.

Data entries may be stored in one or more of the databases 110 of theDPMS 100 or stored in other databases (e.g., databases 140) or systemsoutside of the DPMS 100. The embodiments herein describe data entriesthat may represent discrete events of a subject such as a medicalsymptom, diagnosis, treatment, procedure, gene expression, etc. In someembodiments, data entries may include various types of informationincluding, for example, experimental data (e.g., molecular compounds,lab procedures, or scientific findings), intellectual property (e.g.,patents or trade secrets), contracts (e.g., terms of use for licenseddata), regulatory requirements (e.g., for scientific and financialfilings with a government organization), sensitive information (e.g.,patient information, health records, or human resources data),information technology controls (e.g., usernames, passwords, or othersecurity credentials), among other types of information. A data entrymay have any suitable format for representing discrete or continuousdata, for example, an integer, decimal value, Boolean, string,character, timestamp, etc.

In some embodiments, the indexing system 106 may operate in conjunctionwith one or more other subsystems of the DPMS 100. The security system102 determines and updates authorizations for users to perform varioustypes of interactions with data entries of the DPMS 100. The connectionsystem 104 determines connections between the data entries. In one usecase, responsive to determining that multiple databases include relatedinformation (e.g., data from a hospital, a government agency, or a thirdparty system), the connection system 104 may join or generate a unionbetween data entries stored in the databases, e.g., 110, 140. Thedetection system 108 may classify the data entries. In some embodiments,the detection system 108 assumes that the data entries areself-expressive and determines the classifications based on contents ofthe data entries, e.g., rather than on labels of the data entriesbecause the labels may vary between data sources from different systems.

Each client device 120 comprises one or more computing devices capableof processing data as well as transmitting and receiving data over anetwork 130. For example, a client device 120 may be a desktop computer,a laptop computer, a mobile phone, a tablet computing device, anInternet of Things (IoT) device, or any other device having computingand data communication capabilities. Each client device 120 includes aprocessor for manipulating and processing data, a network connection forcommunicating with other devices, and a storage medium for storing data,program code, and/or program instructions associated with variousapplications. It is noted that a storage medium may include volatilememory (e.g., random access memory) and/or non-volatile storage memorysuch as hard disks, flash memory, and external memory storage devices.

The network 130 may comprise any combination of local area and wide areanetworks employing wired or wireless communication links. In oneembodiment, network 130 uses standard communications technologies andprotocols. For example, network 130 includes communication links usingtechnologies such as Ethernet, 802.11, worldwide interoperability formicrowave access (WiMAX), 3G, 4G, 5G, code division multiple access(CDMA), digital subscriber line (DSL), etc. Examples of networkingprotocols used for communicating via the network 130 includemultiprotocol label switching (MPLS), transmissioncontrol/protocol/Internet protocol (TCP/IP), hypertext transportprotocol (HTTP), simple mail transfer protocol (SMTP), and file transferprotocol (FTP). Data exchanged over the network 130 may be representedusing any format, such as hypertext markup language (HTML) or extensiblemarkup language (XML). In some embodiments, all or some of thecommunication links of the network 130 may be encrypted.

Example System Architecture

FIG. 2 illustrates an example block diagram of the indexing system 106in accordance with one example embodiment. The indexing system 106 mayinclude a processor 200 for manipulating and processing data, a networkconnection for communicating with other devices, and a storage medium210 for storing data and program instructions associated with variousmodules. In one example embodiment, the storage medium 210 comprises anon-transitory computer-readable storage medium. Various executableprograms are each embodied as computer-executable instructions stored tothe non-transitory computer-readable storage medium 210. Theinstructions when executed by the processor 200 cause the indexingsystem 106 to perform the functions attributed to the programs describedherein. Further detail of an example computer system corresponding tothe indexing system 106 is described below with reference to FIG. 6. Thestorage medium 210 includes an indexing engine 214, hash generator 216,user interface engine 218, hash structures 222, and one or moredatabases 224. Alternative embodiments may include different oradditional modules or omit one or more of the illustrated modules.

The indexing engine 214 receives data entries for indexing in one ormore of the databases 224 of the indexing system 106, databases 110 ofthe DPMS 100, or other databases 140 connected to the DPMS 100. Theindexing engine 214 may use an intentionally lossy model to determinewhether or not to index the data entries. Moreover, the indexing engine214 may use the model for database privacy by enhancing anonymization orfor data compression. The model includes a cascade or series of multiplehash structures 222. Generally, a hash structure 222 is a data structurethat implements a hash function to map keys to values of a dataset.Example hash structures 222 include bloom filters or hash tables.

A bloom filter may be applied to determine whether an element is not ina set of elements or potentially in the set of elements. Stateddifferently, the bloom filter may return false positives but not falsenegatives regarding matching (e.g., mapping) elements to the set. In anembodiment, the element is a representation of data entries, e.g., ahash representation or a relative attribute of at least a portion of thedata entries. The set of elements includes representations previouslyseen by (e.g., input to) the bloom filter. The bloom filter maydetermine to filter out representations that have not been previouslyseen by the bloom filter and pass (e.g., not filter) representationsthat have been previously seen by the bloom filter. By using a cascadeof a number of bloom filters that determine whether to filter or passrepresentations of data entries to subsequent bloom filters in thecascade, the indexing system 106 may help improve anonymization of thedata entries by ensuring that the model has seen a threshold number ofinstances of a given representation or set of data entries. As anexample, the threshold number may be eleven, which is implemented by acascade of eleven bloom filters. However, in other embodiments, thecascade may have at least a different threshold number of hashstructures. Since the bloom filters process representations of dataentries and not the data entries themselves, the indexing engine 214 canalso prevent unwanted exposure of sensitive or confidential content ofthe data entries.

A bloom filter may include a fixed size to map an arbitrary number ofelements. As the number of elements processed by the bloom filterincreases, the false positive rate may also increase due to the greaterlikelihood of a collision in the hash function. In some embodiments, theindexing engine 214 tracks a count of data entries added to a bloomfilter. Responsive to determining that the count reaches a thresholdcount, the indexing engine 214 may start or use an additional bloomfilter to reduce the probability of collisions. In other embodiments,the indexing engine 214 uses a different type of hash structure, e.g., ahash table, which does not have a fixed size. In particular, a hashtable may be dynamically resized as the number of processed elementsincreases.

The hash generator 216 generates hash representations of data entriesusing one or more hash functions. A hash function may be a one-wayfunction such that sensitive content of data entries cannot beidentified using the output hash representation, thus enhancingprotection of privacy of the sensitive content during processing by theindexing system 106. The indexing engine 214 may transmit the hashrepresentations to the hash structures 222 for filtering.

In some example embodiments, the indexing engine 214 receives aselection of data elements of one or more data entries for indexing. Thehash generator 216 generates hash representations of the data entriesusing at least the selected data elements. As an example use case, thedata entries describe attributes of a subject and indicate demographics(e.g., age, gender, ethnicity, etc.), geographical location (e.g., city,state, zip code, neighborhood, etc.), diagnosed disease, healthconditions, or medical procedure. A user or administrator of theindexing system 106 may input the selection based on target informationof interest. For instance, the user wants to anonymize information froma population of subjects based on diagnosed disease and geographicallocation, or a certain type of one or more events. As the selectivity ofdata elements increases, the number of instances required to beprocessed by a cascade of hash structures 222 before indexing maydecrease. In other words, without a selection of certain data elements,there may be a large amount of variability in the data entries, whichreduces the likelihood of a given representation of data entries beingpassed through the cascade (e.g., lower rate of collisions/matches inthe hash structures). In some embodiments, the hash structures may savespace by not requiring inverted indexes.

The user interface engine 218 generates user interface layouts andprovides the user interface layouts to client devices 120. Users may usethe client devices 120 to interact with the indexing system 106 via theuser interface layouts. For instance, a user may select data entries forindexing, anonymization, or compression by the indexing system 106. Asan example use case, the user interface engine 218 generates a userinterface layout for a user to select data elements of data entries forthe indexing system 106 to process. In other use cases, a user may querydatabases or query for indexed data entries using a user interface of aclient device 120.

Example Hash Structures

An example use case of anonymizing data entries using a cascade of hashstructures 222 is described below with references to FIGS. 3A-C. Theexample cascade shown in the figures includes at least four hashstructures, though it should be noted that in other embodiments, acascade may include any number of hash structures 222.

FIG. 3A illustrates a diagram of an example cascade of hash structuresin accordance with one embodiment. The indexing system 106 receives dataentries 302 to be potentially indexed into a database. The hashgenerator 216 generates a hash representation Hash A of the data entries302. The indexing engine 214 provides Hash A to a cascade of hashstructures. The first hash structure 310 of the cascade determines if ithas filtered (e.g., previously seen) Hash A. In an embodiment, the hashstructures each include an array of n bits, where n is an integer value.Initially, the arrays of the hash structures are empty and thus have notfiltered Hash A because the indexing system 106 has not yet receiveddata entries for indexing. Over time, the hash structures test hashrepresentations of data entries, and the hash structures map the hashrepresentations to positions in the arrays.

Responsive to determining that the hash structure 310 has not filteredHash A, the hash structure 310 filters Hash A and maps Hash A to aposition in the corresponding array of hash structure 310. The indexingsystem 106 determines not to index the data entries 302 responsive todetermining that hash structure 310 filtered Hash A. Accordingly, theindexing system 106 does not index the data entries 302 because theindexing system 106 cannot anonymize the data entries 302 based on thedata entries received so far by the indexing system 106.

FIG. 3B illustrates another diagram of the example cascade of hashstructures shown in FIG. 3A in accordance with one embodiment. Followingin the same example shown in FIG. 3A, the indexing system 106 receivesdata entries 304 to be potentially indexed into the database. The hashgenerator 216 generates the hash representation Hash A of the dataentries 304. The indexing engine 214 provides Hash A to a cascade ofhash structures. Hash A may be the same hash representation for dataentries 302 (shown in FIG. 3A) and 304, which indicates that dataentries 302 and 304 may have the same or similar content. Responsive todetermining that hash structure 310 filtered Hash A of data entries 302,the hash structure 310 determines to pass Hash A of data entries 304 toa subsequent hash structure 312 in the cascade.

Hash structure 312 receives the passed Hash A from hash structure 310.Responsive to determining that hash structure 312 has not filtered HashA, the hash structure 312 determines to filter Hash A. Thus, theindexing system 106 does not index the data entries 304 because theindexing system 106 cannot anonymize the data entries 304 based on thedata entries received thus far by the indexing system 106 (e.g., dataentries 302 and 304). The indexing system 106 may repeat the processshown in FIGS. 3A-B any number of times to process additional dataentries, and the hash structures may “absorb” the data entries bymapping hash representations of the additional data entries to positionsof the corresponding arrays of the hash structures.

FIG. 3C illustrates yet another diagram of the example cascade of hashstructures shown in FIG. 3A in accordance with one embodiment. Followingin the same example shown in FIG. 3A-B, after the indexing system 106processes multiple data entries, the cascade model reaches a state whereeach hash structure of the cascade has filtered Hash A. The indexingsystem 106 receives data entries 306 to be potentially indexed into thedatabase. The hash generator 216 generates the hash representation HashA of the data entries 306. The indexing engine 214 provides Hash A tothe cascade of hash structures. Hash A may be the same hashrepresentation for data entries 302 (shown in FIG. 3A), 304 (shown inFIG. 3B), and 306.

Since the hash structures 310, 312, and 314 shown in FIG. 3C as well asany other subsequent hash structures in the cascade have filtered HashA, the hash structures pass Hash A to a terminal hash structure 316.Responsive to determining that hash structure 316 filtered Hash A ofdata entries 306, the hash structure 316 determines to index dataentries 304. At this point, the indexing system 106 has processed athreshold amount of data entries (e.g., at least data entries 302, 304,and 306), and thus satisfies a criteria for anonymization of the contentof data entries 306. For example, content of data entries 306 cannot bedirectly or uniquely identified from content aggregated from the otherdata entries, including at least data entries 302 and 304. Additionally,the indexing system 106 may have also compressed data entries 302, 304,and 306 for storage in the database. In particular, the cascade of hashstructures absorbed the processed data entries, which may result innoise reduction because “long tail” statistical events are filtered out.For example, the long tail events represent instances of data that aresufficiently infrequent or low-amplitude in a dataset, e.g., which makesthem statistically insignificant as to be useful for analytic use.

FIG. 3D is a table showing counts of data entries processed by theexample cascade of hash structures shown in FIG. 3A in accordance withone embodiment. The table illustrates that the counts of data entriespassed may generally decrease as the data entries progress through thecascade, e.g., hash structures 310, 312, 314, and 316. Moreover, thecount of data entries indexed by the indexing engine 214 is a fractionof a total count of data entries ingested by the indexing engine 214.The magnitudes of change in counts of data entries passed may varybetween hash structures. For instance, the decrease in data entries fromhash structure 312 to hash structure 314 is the greatest among thechanges shown in the embodiment of FIG. 3D.

FIG. 3E is a partial log of data entries processed by the examplecascade of hash structures shown in FIG. 3A in accordance with oneembodiment. The hash structures 310, 312, 314, and 316 shown in FIGS.3A-D may correspond to the example hash structures 1, 2, 3, and 4 shownin FIG. 3E, respectively. The partial log shown in FIG. 3E may be aportion of a complete log. For instance, the partial log begins at anintermediate point in processing where prior data entries havepreviously been ingested by the cascade of hash structures. The logincludes data elements (e.g., user identifiers, zip code, and gender),statuses, and a date (e.g., as a timestamp). The status indicateswhether a given data entry was cleared or stored (e.g., mapped) by oneof the hash structures, or indexed in a database by the indexing engine214.

FIG. 4 illustrates an example input to a cascade of hash structures inaccordance with one embodiment. The indexing engine 214 receives dataentries 400 including at least data elements 402, 404, 406, and 408. Inaddition, the indexing engine 214 receives a selection of data elements402 and 404 for indexing the data entries 400. As an example use case,the data elements describe attributes of a subject. For instance, thedata elements 402 and 404 indicate a geographical location and adiagnosed disease of the subject, respectively. Other example dataelements may indicate an age or gender of the subject. The hashgenerator 216 generates the hash representation Hash B of the dataentries 400 using the data elements 402 and 404. In particular, the hashgenerator 216 may apply a hash function to values of the selected dataelements and not to unselected data elements. Accordingly, the Hash Bvalue may vary based on which data elements are selected by a user.

In some embodiments, the data elements describe events of a subject,e.g., a medical symptom, diagnosis, treatment, or procedure, associatedwith a timestamp. The hash generator 216 may determine a relativeattribute between the data elements. For example, the hash generator 216determines a temporal distance between timestamps of the data elements402 and 404, indicating a duration of time from an observed symptom to adisease diagnosis of the subject. The hash generator 216 may generate ahash representation using one or more relative attributes of dataelements. Relative attributes may be based on absolute time, referencetime (e.g., day of the week or hour of the day), physical proximity,altitude, semantics, etc.

Following in the above example, the indexing engine 214 provides Hash Bto the hash structure 410 of a cascade. Responsive to determining thathash structure 410 filtered Hash B of data entries 400, the hashstructure 410 determines to pass Hash B to a subsequent hash structure412 in the cascade. Responsive to determining that hash structure 412has not filtered Hash B, the hash structure 412 determines to filterHash B. Given the current state of the cascade, the indexing engine 214does not index the data entries 400 because the indexing engine 214 hasnot yet satisfied a criteria for anonymizing the data entries 400, e.g.,based on the selected data elements 402 and 404.

In some embodiments, the indexing engine 214 may index portions of adata entry that satisfy a privacy criteria even though the data entry asa whole may not necessarily satisfy the privacy criteria. For example,the data entries may include data elements describing a user's name,geographical location, gender, and age. Each hash structure of a cascademay determine to pass the data entries when hashing a subset of the dataelements, e.g., the indexing engine 214 is able to anonymize based onname and geographical location. However, the indexing engine 214 may notnecessarily be able to anonymize based on gender or age (e.g., at agiven point in time using an initial set of data entries). Accordingly,the indexing engine 214 may determine to index the name and geographicallocation information of users but to not index the gender or ageinformation of the users, e.g., until further data entries are processedthat satisfy a criteria for protection of the gender or age information.

Example Process Flow

FIG. 5 illustrates an example process flow 500 for anonymizing dataentries in accordance with one embodiment. The process 500 may includedifferent or additional steps than those described in conjunction withFIG. 5 in some embodiments or perform steps in different orders than theorder described in conjunction with FIG. 5. The indexing engine 214 ofthe indexing system 106 receives 502 data entries for indexing in adatabase. The hash generator 216 generates 504 a hash representation ofthe data entries. In some embodiments, the indexing engine 214 receivesthe hash representation, which may be predetermined or generated by asource external to the indexing system 106. In some embodiments, theindexing engine 214 receives, from a client device 120 of a user, aselection of multiple data elements (e.g., a subset) of the dataentries. The hash generator 216 may generate the hash representationusing the selected data elements.

The indexing engine 214 transmits 506 the hash representation to acascade of hash structures 222 of the indexing system 106. At least oneof the hash structures 222 is configured to determine 508 to pass thehash representation responsive to determining that the hash structurepreviously filtered another instance of the hash representation. Thehash structure is further configured to transmit 510 the hashrepresentation to a subsequent hash structure in the cascade responsiveto the determination to pass the hash representation. The indexingengine 214 indexes 512 the data entries into the database responsive todetermining that each hash structure of the cascade determined to passthe hash representation. The indexing engine 214 may transmit anotification to a client device 120 indicating that the data entrieshave been indexed into the database.

In an embodiment, the indexing engine 214 generates or receives anotherhash representation of another set of data entries (e.g., different thanthe hash representation that was passed as described above). Theindexing engine 214 transmits the other hash representation to thecascade of hash structures. The indexing engine 214 determines not toindex the other set of data entries responsive to determining that oneof the hash structures of the cascade does not pass the other hashrepresentation. For instance, the indexing engine 214 has not yetsatisfied a criteria for anonymization of the other set of data entriesbased on the hash representations filtered so far by the hashstructures. In some embodiments, the data entries that are not passed bythe hash structures may be recovered in an emergency protocol. Forexample, in addition to generating hash representation of data entries,the hash generator 216 may also store the data entries in a buffersecured using encryption. In some embodiments, the hash generator 216encrypts the data entries using a public key provided by a third party.Further, the data entries may be stored on the DPMS 100 or on anothersystem, which may be associated with the third party. As an example usecase, the indexing system 106 is used for protecting user informationfor a clinical trial. Responsive to a user-specific or patient-specificevent, e.g., during or after the clinical trial, the indexing system 106may recover data entries from the secured buffer though a third party orby the holder of the associated private key.

Physical Components

FIG. 6 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in one or more processors (or controllers) in accordance with oneexample embodiment. The instructions (e.g., program code) may correspondto the process, for example, described in FIG. 5. The instructions alsomay correspond to the components/modules carrying out the functionalitydisclosed in FIGS. 1-4.

Specifically, FIG. 6 shows a diagrammatic representation of an exampleform of a computer system 600. The computer system 600 can be used toexecute instructions 624 (e.g., structured as program code or software)for causing the machine to perform any one or more of the methodologies(or processes) described herein, for example, in FIGS. 1-5. The machinemay operate as a standalone device or a connected (e.g., networked)device that connects to other machines. In a networked deployment, themachine may operate in the capacity of a server machine or a clientmachine in a server-client network environment, or as a peer machine ina peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a smartphone, aninternet of things (IoT) appliance, a network router, switch or bridge,or any machine capable of executing instructions 624 (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute instructions 624 to perform any one or more of themethodologies discussed herein. In addition, it is noted that not allthe components noted in FIG. 6 may be necessary for a machine to beconfigured to execute the systems and/or processes described within thedisclosure.

The example computer system 600 includes one or more processing units(generally processor 602). The processor 602 is, for example, a centralprocessing unit (CPU), a graphics processing unit (GPU), a digitalsignal processor (DSP), a controller, a state machine, one or moreapplication specific integrated circuits (ASICs), one or moreradio-frequency integrated circuits (RFICs), or any combination ofthese. The processor 602 may be similar to processor 200. The computersystem 600 also includes a main memory 604. The computer system mayinclude a storage unit 616. The processor 602, memory 604, and thestorage unit 616 communicate via a bus 608.

In addition, the computer system 600 can include a static memory 606, agraphics display 610 (e.g., to drive a plasma display panel (PDP), aliquid crystal display (LCD), or a projector). The computer system 600may also include alphanumeric input device 612 (e.g., a keyboard), acursor control device 614 (e.g., a mouse, a trackball, a joystick, amotion sensor, or other pointing instrument), a signal generation device618 (e.g., a speaker), and a network interface device 620, which alsoare configured to communicate via the bus 608.

The storage unit 616 includes a machine-readable medium 622 on which isstored instructions 624 (e.g., software) embodying any one or more ofthe methodologies or functions described herein. The instructions 624may also reside, completely or at least partially, within the mainmemory 604 or within the processor 602 (e.g., within a processor's cachememory) during execution thereof by the computer system 600, the mainmemory 604 and the processor 602 also constituting machine-readablemedia. The instructions 624 may be transmitted or received over anetwork 626 via the network interface device 620.

While machine-readable medium 622 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 624. The term “machine-readable medium” shall also betaken to include any medium that is capable of storing instructions 624for execution by the machine and that cause the machine to perform anyone or more of the methodologies disclosed herein. The term“machine-readable medium” includes, but not be limited to, datarepositories in the form of solid-state memories, optical media, andmagnetic media.

Additional Considerations

The disclosed configuration provides benefits and advantages thatinclude, for example, anonymizing, indexing, or compressing data entriesin database using hash structures such as bloom filters or hash tables.These benefits include the recovery of one or more hashed data entriesunder approved circumstances through a third party or by decryption bythe holder of the associated private key. Additional benefits andadvantages may include processing, by the hash structures, hashrepresentations of data entries or relative attributes between dataentries. Thus, in example use cases, these advantages may enable drugdevelopment, treatments, or medical research while enhancing protectionof the privacy of sensitive data entries indexed in databases or fromother data sources.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms, for example, as illustrated inFIGS. 1-2. Modules may constitute either software modules (e.g., codeembodied on a machine-readable medium) or hardware modules. A hardwaremodule is tangible unit capable of performing certain operations and maybe configured or arranged in a certain manner. In example embodiments,one or more computer systems (e.g., a standalone, client or servercomputer system) or one or more hardware modules of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwaremodule that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors, e.g.,processor 200 or processor 602, that are temporarily configured (e.g.,by software) or permanently configured to perform the relevantoperations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented modules that operate toperform one or more operations or functions. The modules referred toherein may, in some example embodiments, comprise processor-implementedmodules.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs)).

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve physical manipulation of physicalquantities. Typically, but not necessarily, such quantities may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process for indexing data entries (e.g., for enhancingprivacy, anonymization, or data compression) that may be executedthrough the disclosed principles herein. Thus, while particularembodiments and applications have been illustrated and described, it isto be understood that the disclosed embodiments are not limited to theprecise construction and components disclosed herein. Variousmodifications, changes and variations, which will be apparent to thoseskilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by an indexing system from a client device, a selection of aplurality of data elements of a plurality of data entries; receiving ahash representation of the plurality of data entries generated using theselected plurality of data elements; transmitting the hashrepresentation to a cascade of bloom filters of the indexing system, thecascade of bloom filters including a plurality of bloom filters and aterminal bloom filter, each bloom filter of the cascade of bloom filtersincluding an array of n bits, wherein n is an integer value, the cascadeof bloom filters mapping hash representations to positions in thearrays, each bloom filter of the plurality of bloom filters configuredto: determine to pass the hash representation responsive to determiningthat the bloom filter previously filtered another instance of the hashrepresentation, wherein the bloom filter returns false positives but notfalse negatives regarding matching an element to a set of elements, andtransmit the hash representation to a subsequent bloom filter in thecascade of bloom filters for filtering or passing to another subsequentone of the bloom filters responsive to the determination to pass thehash representation; and wherein at least one bloom filter of theplurality of bloom filters transmits the hash representation to theterminal bloom filter; determining that the plurality of data elementsis anonymized in a database responsive to a determination by theterminal bloom filter to pass the hash representation, wherein theterminal bloom filter outputs the plurality of data entries thatsatisfies a criteria for anonymization; indexing the plurality of dataentries into the database responsive to the determination that theplurality of data elements is anonymized in the database; andtransmitting a notification by the indexing system to the client device,the notification indicating that the plurality of data entries have beenindexed into the database.
 2. The computer-implemented method of claim1, further comprising: receiving an additional hash representation of anadditional plurality of data entries; transmitting the additional hashrepresentation to the cascade of bloom filters; and determining not toindex the additional plurality of data entries responsive to determiningthat one of the bloom filters of the cascade does not pass theadditional hash representation.
 3. The computer-implemented method ofclaim 1, wherein the plurality of data entries includes a first dataentry and a second data entry describing a first event and a secondevent, respectively, the method further comprising: determining arelative attribute between the first data entry and the second dataentry, wherein the relative attribute represents a temporal distancebetween timestamps of the first event and the second event, the hashrepresentation generated using at least the relative attribute.
 4. Thecomputer-implemented method of claim 1, wherein the database includes acompressed version of the plurality of data entries after indexing theplurality of data entries into the database.
 5. The computer-implementedmethod of claim 1, wherein the selected plurality of data elementsindicates at least a geographical location and a disease diagnosis ofone or more subjects.
 6. The computer-implemented method of claim 1,wherein the cascade of bloom filters includes a series of at leasteleven bloom filters.
 7. The computer-implemented method of claim 1,wherein the criteria is associated with a threshold number of dataentries describing a population of subjects having a same geographicallocation and disease diagnosis.
 8. A non-transitory computer-readablestorage medium comprising stored program code for indexing of dataentries in an indexing system, the program code when executed by atleast one processor causes the processor to: receive, from a clientdevice, a selection of a plurality of data elements of a plurality ofdata entries; receive a hash representation of the plurality of dataentries generated using the selected plurality of data elements;transmit the hash representation to a cascade of bloom filters of theindexing system, the cascade of bloom filters including a plurality ofbloom filters and a terminal bloom filter, each bloom filter of thecascade of bloom filters including an array of n bits, wherein n is aninteger value, the cascade of bloom filters mapping hash representationsto positions in the arrays, each bloom filter of the plurality of bloomfilters configured to: determine to pass the hash representationresponsive to determining that the bloom filter previously filteredanother instance of the hash representation, wherein the bloom filterreturns false positives but not false negatives regarding matching anelement to a set of elements, and transmit the hash representation to asubsequent bloom filter in the cascade of bloom filters for filtering orpassing to another subsequent one of the bloom filters responsive to thedetermination to pass the hash representation; and wherein at least onebloom filter of the plurality of bloom filters transmits the hashrepresentation to the terminal bloom filter; determine that theplurality of data elements is anonymized in a database responsive to adetermination by the terminal bloom filter to pass the hashrepresentation, wherein the terminal bloom filter outputs the pluralityof data entries that satisfies a criteria for anonymization; index theplurality of data entries into the database responsive to thedetermination that the plurality of data elements is anonymized in thedatabase; and transmit a notification by the indexing system to theclient device, the notification indicating that the plurality of dataentries have been indexed into the database.
 9. The non-transitorycomputer-readable storage medium of claim 8, further comprising programcode that when executed causes the processor to: generate an additionalhash representation of an additional plurality of data entries; transmitthe additional hash representation to the cascade of bloom filters; anddetermine not to index the additional plurality of data entriesresponsive to determining that one of the bloom filters of the cascadedoes not pass the additional hash representation.
 10. The non-transitorycomputer-readable storage medium of claim 8, wherein the selectedplurality of data elements indicates at least a geographical locationand a disease diagnosis of one or more subjects.
 11. The non-transitorycomputer-readable storage medium of claim 8, wherein the cascade ofbloom filters includes a series of at least eleven bloom filters. 12.The non-transitory computer-readable storage medium of claim 8, furthercomprising program code that when executed causes the processor to:determine a relative attribute between a first data entry and a seconddata entry of the plurality of data entries, the hash representationgenerated using at least the relative attribute.
 13. The non-transitorycomputer-readable storage medium of claim 12, wherein the first dataentry and the second data entry describe a first event and a secondevent, respectively, and wherein the relative attribute represents atemporal distance between timestamps of the first event and the secondevent.
 14. The non-transitory computer-readable storage medium of claim8, wherein the database includes a compressed version of the pluralityof data entries after indexing the plurality of data entries into thedatabase.
 15. The non-transitory computer-readable storage medium ofclaim 8, wherein the criteria is associated with a threshold number ofdata entries describing a population of subjects having a samegeographical location and disease diagnosis.